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Article

Applying Deep Learning to Clear-Sky Radiance Simulation for VIIRS with Community Radiative Transfer Model—Part 1: Develop AI-Based Clear-Sky Mask

1
Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD 20740, USA
2
Center for Satellite Applications and Research (STAR), National Environmental Satellite, Data, and Information Service (NESDIS), National Oceanic and Atmospheric Administration (NOAA), College Park, MD 20740, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2021, 13(2), 222; https://doi.org/10.3390/rs13020222
Received: 8 December 2020 / Revised: 5 January 2021 / Accepted: 6 January 2021 / Published: 11 January 2021
(This article belongs to the Section AI Remote Sensing)
A fully connected deep neural network (FCDN) clear-sky mask (CSM) algorithm (FCDN_CSM) was developed to assist the FCDN-based Community Radiative Transfer Model (FCDN_CRTM) to reproduce the Visible Infrared Imaging Radiometer Suite (VIIRS) clear-sky radiances in five thermal emission M (TEB/M) bands. The model design was referenced and enhanced from its earlier version (version 1), and was trained and tested in the global ocean clear-sky domain using six dispersion days’ data from 2019 to 2020 as inputs and a modified NOAA Advanced Clear-Sky Processor over Ocean (ACSPO) CSM product as reference labels. The improved FCDN_CSM (version 2) was further enhanced by including daytime data, which was not collected in version 1. The trained model was then employed to predict VIIRS CSM over multiple days in 2020 as an accuracy and stability check. The results were validated against the biases between the sensor observations and CRTM calculations (O-M). The objectives were to (1) enhance FCDN_CSM performance to include daytime analysis, and improve model stability, accuracy, and efficiency; and (2) further understand the model performance based on a combination of the statistics and physical interpretation. According to the analyses of the F-score, the prediction result showed ~96% and ~97% accuracy for day and night, respectively. The type Cloud was the most accurate, followed by Clear-Sky. The O-M mean biases are comparable to the ACSPO CSM for all bands, both day and night. The standard deviations (STD) were slightly degraded in long wave IRs (M14, M15, and M16), mainly due to contamination by a 3% misclassification of the type Cloud, which may require the model to be further fine-tuned to improve prediction accuracy in the future. However, the consistent O-M means and STDs persist throughout the prediction period, suggesting that FCDN_CSM version 2 is robust and does not have significant overfitting. Given its high F-scores, spatial and long-term stability for both day and night, high efficiency, and acceptable O-M means and STDs, FCDN_CSM version 2 is deemed to be ready for use in the FCDN_CRTM. View Full-Text
Keywords: fully connected “Deep” neural network (FCDN); clear-sky mask (CSM); community radiative transfer model (CRTM); deep learning; machine learning; artificial neural network (ANN); the visible infrared imaging radiometer suite (VIIRS); Advanced Clear-Sky Processor over Ocean (ACSPO) fully connected “Deep” neural network (FCDN); clear-sky mask (CSM); community radiative transfer model (CRTM); deep learning; machine learning; artificial neural network (ANN); the visible infrared imaging radiometer suite (VIIRS); Advanced Clear-Sky Processor over Ocean (ACSPO)
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MDPI and ACS Style

Liang, X.; Liu, Q. Applying Deep Learning to Clear-Sky Radiance Simulation for VIIRS with Community Radiative Transfer Model—Part 1: Develop AI-Based Clear-Sky Mask. Remote Sens. 2021, 13, 222. https://doi.org/10.3390/rs13020222

AMA Style

Liang X, Liu Q. Applying Deep Learning to Clear-Sky Radiance Simulation for VIIRS with Community Radiative Transfer Model—Part 1: Develop AI-Based Clear-Sky Mask. Remote Sensing. 2021; 13(2):222. https://doi.org/10.3390/rs13020222

Chicago/Turabian Style

Liang, Xingming, and Quanhua Liu. 2021. "Applying Deep Learning to Clear-Sky Radiance Simulation for VIIRS with Community Radiative Transfer Model—Part 1: Develop AI-Based Clear-Sky Mask" Remote Sensing 13, no. 2: 222. https://doi.org/10.3390/rs13020222

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